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FlowBank introduces a three-stage framework for optimizing agentic workflows in LLM multi-agent systems by precomputing a diverse set of reusable workflows and adaptively selecting the best one per query, achieving higher scores while maintaining cost competitiveness.
This paper presents a decision support framework for optimizing validator selection in proof-of-stake blockchains, balancing portfolio quality and diversification through multi-objective optimization and interactive preference learning.
This paper introduces Semantic State Abstraction Interfaces (SSAI) to separate representation hypotheses from optimization variance in LLM-augmented portfolio decisions. It concludes that SSAI's apparent advantage is largely a basket-selection effect, with dense encodings and principal components performing better empirically.